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Jedidiah Carlson, Ph.D.

Population Genetics | Bioinformatics | Metaresearch

I am a postdoctoral research fellow in Kelley Harris' lab at the University of Washington Department of Genome Sciences. I completed my Ph.D. in Bioinformatics and my M.S. in Biostatistics in Sebastian Zöllner’s lab at the University of Michigan. I am broadly interested in the following (sometimes overlapping) areas of research:

Population Genetics

The extensive genetic variation we see throughout the tree of life is ultimately due to the billions of spontaneous mutations that arise in each new generation. Understanding the processes which create heritable variation is central to understanding the very core of evolutionary change. I use computational and statistical methods to study how and why mutation rates vary throughout the genome, between individuals, and over evolutionary timescales, and how this variation impacts past and ongoing changes to our DNA.

Bioinformatics

Modern genomic datasets are massive and messy, often comprising thousands of genomes sequenced in multiple batches on different sequencing machines. Uncovering biological insights from such datasets increasingly relies on the development and implementation of robust, scalable bioinformatics tools and rigorous quality control. My research in this area has focused on developing quality control tools and auditing published results to identify anomalous patterns in the data that might lead to inaccurate or spurious findings in downstream analyses.

Metaresearch

Like many scientists, I am fairly active on Twitter. Working in human population genetics, a field that informs and challenges our identity as a species and as individuals, I am no stranger to the heated debates and controversies that arise when research collides with social media. This experience has led me to more formally study how scientific research gets discussed and distributed on social media. I am interested in using machine learning and text mining approaches to better understand: (1) how we can use altmetrics more effectively in research evaluation, (2) how social media can be harnessed to trace the potential societal impacts of new scientific research, and (3), how we as scientists can use this information to promote better scientific literacy among the broader public.

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